Applied Statistics with R by David Dalpiaz
Applied Statistics with R - Table of Contents
- 1. Introduction
- 2. Introduction to R
- 3. Data and Programming Concepts
- 4. Summarizing Data and Visualizations
- 5. Probability and Statistics in R
- 6. Essential R Resources
- 7. Simple Linear Regression
- 8. Inference for Simple Linear Regression
- 9. Multiple Linear Regression
- 10. Fundamentals of Model Building
- 11. Categorical Predictors and Interactions
- 12. Principles of Analysis of Variance
- 13. Model Diagnostics and Residuals
- 14. Data Transformations
- 15. Detecting Collinearity
- 16. Variable Selection and Model Building
- 17. Introduction to Logistic Regression
- 18. Advanced Topics and Beyond
What You Will Learn in Applied Statistics with R
Applied Statistics with R by David Dalpiaz is a highly practical, modern open-access textbook designed to guide students through the mechanics of empirical data analysis and predictive modeling. Developed for applied statistics courses, this foundational text breaks down essential concepts like exploratory data analysis, simple and multiple linear regression, dummy variables, model diagnostics, collinearity, and analysis of variance (ANOVA) into accessible, step-by-step computational lessons.
Perfect for undergraduate and graduate students in statistics, data science, computer science, and quantitative social sciences, this book bridges the gap between theoretical statistical inference and modern programming practice. Dalpiaz systematically walks readers through model fitting, hypothesis testing, interaction terms, variable selection, and logistic regression using native R capabilities. Whether evaluating residual plots or comparing complex nested models, learners will find this structured textbook invaluable.
Recognized for its clear explanation, coding workflows, and pedagogical structure, it remains one of the best applied statistics with R books pdf available for self-study. It systematically equips readers with the necessary analytical tools for mastering statistical modeling in R with confidence.
Book Details & Specifications
Title:
Applied Statistics with R by David Dalpiaz
Publisher:
Self Publishing
Year:
2021
Pages:
457
Type:
PDF
Language:
English
ISBN-10 #:
0198869975
ISBN-13 #:
978-0198869979
License:
CC BY-NC-SA 4.0
Amazon:
Amazon
About the Author: David Dalpiaz
The author David Dalpiaz
is a Teaching Associate Professor in the Department of Statistics at the University of Illinois Urbana-Champaign (UIUC). He completed his Ph.D. in Statistics at the University of Illinois, focusing on statistical pedagogy, machine learning, and computational data science.
An active advocate for open-access educational resources, Professor Dalpiaz has authored multiple online interactive textbooks and packages for statistical computing. His work on applied regression modeling and computational statistics with R provides learners worldwide with a clear, practical, and hands-on foundation in data analysis.
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